Why multiple plots per figure matter in Matplotlib - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this code with multiple subplots?

Look at the code below that creates two plots in one figure using matplotlib. What will be the shape of the axes object?

Matplotlib

import matplotlib.pyplot as plt
fig, axes = plt.subplots(1, 2)
print(type(axes))
print(axes.shape)

A <class 'numpy.ndarray'>\n(2,)

B <class 'numpy.ndarray'>\n(1, 2)

C <class 'list'>\n(2,)

D <class 'matplotlib.axes._subplots.AxesSubplot'>\n()

💡 Hint

When you create multiple subplots in one row and multiple columns, axes is a 1D array with shape (2,).

The plt.subplots(1, 2) creates 1 row and 2 columns of plots. The axes object is a numpy array with shape (2,).

❓ data_output

intermediate

How many lines are plotted in this figure with multiple subplots?

This code creates a figure with 2 rows and 1 column of plots. Each subplot plots two lines. How many lines are drawn in total?

Matplotlib

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0, 10, 100)
fig, axes = plt.subplots(2, 1)
for ax in axes:
    ax.plot(x, np.sin(x))
    ax.plot(x, np.cos(x))
print(sum(len(ax.lines) for ax in axes))

A 2

B 1

C 4

D 3

💡 Hint

Count how many lines each subplot has, then multiply by the number of subplots.

Each subplot has 2 lines plotted (sin and cos). There are 2 subplots, so total lines = 2 * 2 = 4.

❓ visualization

advanced

Which option correctly creates a figure with 3 plots arranged in one row?

Choose the code that creates a figure with 3 side-by-side plots using matplotlib. The plots should share the same y-axis.

A fig, axes = plt.subplots(3, 1, sharex=True)

B fig, axes = plt.subplots(3, 1, sharey=True)

C fig, axes = plt.subplots(1, 3, sharex=True)

D fig, axes = plt.subplots(1, 3, sharey=True)

💡 Hint

Remember, rows come first, then columns in plt.subplots(rows, columns).

To create 3 plots side-by-side, you want 1 row and 3 columns. Sharing y-axis means sharey=True.

🔧 Debug

advanced

What error does this code raise when creating multiple plots?

What error will this code raise?

import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 2)
axes.plot([1, 2, 3], [4, 5, 6])

A AttributeError: 'numpy.ndarray' object has no attribute 'plot'

B TypeError: plot() missing 1 required positional argument

C ValueError: too many values to unpack

D No error, plots successfully

💡 Hint

Remember that axes is an array of subplots, not a single subplot.

axes is a 2x2 numpy array of Axes objects. You cannot call plot directly on the array. You must call it on one Axes element.

🚀 Application

expert

Why use multiple plots per figure in data analysis?

Which of the following is the best reason to use multiple plots in one figure?

A To compare different data sets side-by-side easily and spot patterns or differences

B To reduce the total number of figures saved on disk

C To make the code run faster by plotting multiple graphs at once

D To avoid using legends in plots

💡 Hint

Think about how seeing plots together helps understanding data.

Multiple plots per figure help compare data sets visually in one view, making it easier to find patterns or differences.